AI Article Synopsis

  • The review explores the concept of retinal age as a new biomarker derived from retinal images that can help estimate biological age and assess deviations from normal aging through a metric called retinal age gap (RAG).
  • It analyzes 13 recent studies from 2022 to 2023, identifying four different models that accurately predict biological age using retinal images, with similar performance metrics indicating their potential utility.
  • The findings suggest that higher RAG is linked to negative health outcomes, such as increased mortality and cardiovascular issues, and emphasize the need for more research to confirm the clinical application of these tools, especially in neuropsychiatry.

Article Abstract

Background/aims: The emerging concept of retinal age, a biomarker derived from retinal images, holds promise in estimating biological age. The retinal age gap (RAG) represents the difference between retinal age and chronological age, which serves as an indicator of deviations from normal ageing. This scoping review aims to collate studies on retinal age to determine its potential clinical utility and to identify knowledge gaps for future research.

Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist, eligible non-review, human studies were identified, selected and appraised. PubMed, Scopus, SciELO, PsycINFO, Google Scholar, Cochrane, CINAHL, Africa Wide EBSCO, MedRxiv and BioRxiv databases were searched to identify literature pertaining to retinal age, the RAG and their associations. No restrictions were imposed on publication date.

Results: Thirteen articles published between 2022 and 2023 were analysed, revealing four models capable of determining biological age from retinal images. Three models, 'Retinal Age', 'EyeAge' and a 'convolutional network-based model', achieved comparable mean absolute errors: 3.55, 3.30 and 3.97, respectively. A fourth model, 'RetiAGE', predicting the probability of being older than 65 years, also demonstrated strong predictive ability with respect to clinical outcomes. In the models identified, a higher predicted RAG demonstrated an association with negative occurrences, notably mortality and cardiovascular health outcomes.

Conclusion: This review highlights the potential clinical application of retinal age and RAG, emphasising the need for further research to establish their generalisability for clinical use, particularly in neuropsychiatry. The identified models showcase promising accuracy in estimating biological age, suggesting its viability for evaluating health status.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344507PMC
http://dx.doi.org/10.1136/bmjophth-2024-001794DOI Listing

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